Funding Agency: Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA)
Program: New Directions Research Program (NDRP)
Goal: Generate new knowledge on the impacts of land use conversion on soil properties and GHG emission through an integrated approach using advanced remote sensing technologies combined with multi-scale modeling.
Objectives:
(1) Develop methodologies for detecting and mapping land cover/use dynamics in Northern Ontario
(2) Develop methodologies to characterize soil properties in forest and agricultural lands by exploiting advanced remotely sensed data, and
(3) Assess how the initial forest and soil properties and subsequent management schemes affect the change of soil carbon stock and GHG emission due to land use conversion.
Publication
Ituen, I. and B. Hu, 2021. An Automatic and Operational Method to Predict Landcover change using spatiotemporal MODIS data: A case study in Northern Ontario, Canada. ISPRS International Journal of Geo-Information, 10 (5), May 2021. Link: https://mdpi-res.com/d_attachment/ijgi/ijgi-10-00325/article_deploy/ijgi-10-00325-v2.pdf?version=1620788001
Pittman, R., B. Hu, and K. Webster, 2020. Improvement of soil properties classification for a boreal biome using multi-source remotely-sensed data, Geoderma, 381, 114760
Pittman, R, Hu, B., Sohn, G. 2021. Determination of regions suitable for agriculture in the Gordon Cosens Forest of Ontario by means of analytical hierarchy process with fuzzy logic inference. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. June 2021. Link: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2021/623/2021/
Ituen, I. and B. Hu, 2021. Estimating carbon and greenhouse gas emissions in remote regions of Canada, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., June 2021. Link:
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2021/575/2021/
Hu. B and W. Jung, 2021. Individual tree crown delineation from high spatial resolution imagery using u-net. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., June 2021. Link:
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2021/61/2021/
Pittman, R. and B. Hu,2020. Estimation of soil bulk density and carbon using multi-source remotely sensed data, the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Link:
https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2020/541/2020/
Pittman, R. and B. Hu, 2020. Improvement of soil texture classification with lidar data, Proceedings of IEEE International Geoscience and Remote Sensing Symposium, 2020. Link:
https://ieeexplore.ieee.org/document/9324152
